Uncertainty quantification (UQ) have been boosted by recent advances is associated algorithms and software, as well as increased computational abilities. As a result, it has become possible to address uncertainties in climate models. However, many challenges remain when dealing with complex, highly nonlinear models. Here we highlight recent work using the Community Land Model (CLM), and propose a framework for more efficient UQ analysis using modules of CLM rather than the full model. Climate models are computationally intensive. This necessarily disqualifies pure Monte-Carlo algorithms for uncertainty estimation, since naive Monte-Carlo approaches require too many sampled simulations for reasonable accuracy. In this work, we build computationally inexpensive surrogate models in order to accelerate both forward and inverse UQ methods. Next, climate models typically suffer from the curse of dimensionality. For example, the CLM depends on more than 80 input parameters with somewhat uncertain values. We apply Bayesian compressive sensing (BCS) techniques in order to infer the best basis set for the PC surrogate model. BCS performs particularly well in high-dimensional settings when model simulations are very sparse. Finally, as climate models can, and the CLM in particular does, exhibit sharp transients with varying input parameters, we consider multi-cluster PC representations in which spectral expansions are obtained within each sample set class, and combined accordingly using classification techniques. The ultimate, multi-cluster PC surrogate model allows a global, variance-based sensitivity analysis that can drastically reduce the input parameter space dimensionality. Also, the PC surrogate model can be invoked, without much computational overhead, in place of the full climate model, in optimization or calibration studies that require prohibitively many forward model simulations. In particular, we use PC surrogates to infer input parameter distributions given physical observations. At this stage, adaptive Markov chain Monte-Carlo (MCMC) algorithms are used to explore the input parameter space efficiently. In future model development efforts, land model code will be modularized to the extent where it is possible to perform Monte-Carlo algorithms directly on model subroutines in some cases, or to construct lower-dimensional, higher-accuracy surrogate models for specific model processes. Here we investigate the use of MCMC to quantify uncertainty about parameters related to plant photosynthesis.